scholarly journals PUBLIC’S SENTIMENT ANALYSIS ON SHOPEE-FOOD SERVICE USING LEXICON-BASED AND SUPPORT VECTOR MACHINE

2021 ◽  
Vol 4 (1) ◽  
pp. 1-8
Author(s):  
Shafira Shalehanny ◽  
Agung Triayudi ◽  
Endah Tri Esti Handayani
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Food Service ◽  
Support Vector ◽  
Accuracy Score ◽  
Online Business ◽  
Data Source ◽  
Sentiment Score ◽  
Processing Language

Technology field following how era keep evolving. Social media already on everyone’s daily life and being a place for writing their opinion, either review or response for product and service that already being used. Twitter are one of popular social media on Indonesia, according to Statista data it reach 17.55 million users. For online business sector, knowing sentiment score are really important to stepping up their business. The use of machine learning, NLP (Natural Processing Language), and text mining for knowing the real meaning of opinion words given by customer called sentiment analysis. Two methods are using for data testing, the first is Lexicon Based and the second is Support Vector Machine (SVM). Data source that used for sentiment analyst are from keyword ‘ShopeeFood’ and ‘syopifud’. The result of analysis giving accuracy score 87%, precision score 81%, recall score 75%, and f1-score 78%.

scholarly journals Analisis Sentimen Data Twitter Tentang Pasangan Capres-Cawapres Pemilu 2019 Dengan Metode Lexicon Based Dan Support Vector Machine

2019 ◽  
Vol 11 (2) ◽  
pp. 144
Author(s):  
Danar Wido Seno ◽  
Arief Wibowo
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Vice President ◽  
Training Data ◽  
Support Vector ◽  
New Words ◽  
Textual Data ◽  
Data Content ◽  
Combination Of Methods

Social media writing content growing make a lot of new words that appear on Twitter in the form of words and abbreviations that appear so that sentiment analysis is increasingly difficult to get high accuracy of textual data on Twitter social media. In this study, the authors conducted research on sentiment analysis of the pairs of candidates for President and Vice President of Indonesia in the 2019 Elections. To obtain higher accuracy results and accommodate the problem of textual data development on Twitter, the authors conducted a combination of methods to conduct the sentiment analysis with unsupervised and supervised methods. namely Lexicon Based. This study used Twitter data in October 2018 using the search keywords with the names of each pair of candidates for President and Vice President of the 2019 Elections totaling 800 datasets. From the study with 800 datasets the best accuracy was obtained with a value of 92.5% with 80% training data composition and 20% testing data with a Precision value in each class between 85.7% - 97.2% and Recall value for each class among 78, 2% - 93.5%. With the Lexicon Based method as a labeling dataset, the process of labeling the Support Vector Machine dataset is no longer done manually but is processed by the Lexicon Based method and the dictionary on the lexicon can be added along with the development of data content on Twitter social media.

Sentiment Analysis with Social Media Analytics, Methods, Process, and Applications

2017 ◽  
pp. 192-208
Author(s):  
Karteek Ramalinga Ponnuru ◽  
Rashik Gupta ◽  
Shrawan Kumar Trivedi
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Support Vector ◽  
Social Media Analytics ◽  
Nearest Neighbour ◽  
Huge Amount ◽  
Word Clouds

Firms are turning their eye towards social media analytics to get to know what people are really talking about their firm or their product. With the huge amount of buzz being created online about anything and everything social media has become ‘the' platform of the day to understand what public on a whole are talking about a particular product and the process of converting all the talking into valuable information is called Sentiment Analysis. Sentiment Analysis is a process of identifying and categorizing a piece of text into positive or negative so as to understand the sentiment of the users. This chapter would take the reader through basic sentiment classifiers like building word clouds, commonality clouds, dendrograms and comparison clouds to advanced algorithms like K Nearest Neighbour, Naïve Biased Algorithm and Support Vector Machine.

Ooredoo Rayek

2020 ◽  
Vol 11 (2) ◽  
pp. 66-81
Author(s):  
Badia Klouche ◽  
Sidi Mohamed Benslimane ◽  
Sakina Rim Bennabi
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Experimental Results ◽  
Support Vector ◽  
Textual Data ◽  
New Strategy ◽  
Set Up

Sentiment analysis is one of the recent areas of emerging research in the classification of sentiment polarity and text mining, particularly with the considerable number of opinions available on social media. The Algerian Operator Telephone Ooredoo, as other operators, deploys in its new strategy to conquer new customers, by exploiting their opinions through a sentiments analysis. The purpose of this work is to set up a system called “Ooredoo Rayek”, whose objective is to collect, transliterate, translate and classify the textual data expressed by the Ooredoo operator's customers. This article developed a set of rules allowing the transliteration from Algerian Arabizi to Algerian dialect. Furthermore, the authors used Naïve Bayes (NB) and (Support Vector Machine) SVM classifiers to assign polarity tags to Facebook comments from the official pages of Ooredoo written in multilingual and multi-dialect context. Experimental results show that the system obtains good performance with 83% of accuracy.

scholarly journals Studi Komparatif Metode Ekstraksi Fitur pada Analisis Sentimen Maskapai Penerbangan Menggunakan Support Vector Machine dan Maximum Entropy

2019 ◽  
Vol 3 (3) ◽  
pp. 402-407 ◽  
Author(s):  
Mona Cindo ◽  
Dian Palupi Rini ◽  
Ermatita
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Maximum Entropy ◽  
Entropy Method ◽  
Support Vector ◽  
Machine Method ◽  
N Gram ◽  
Almost All

Almost all companies use social media to improve their product services and provide after-sales services that allow their customers to review the quality of their products. By using Twitter social media to be an important source for tracking sentiment analysis. Sentiment analysis is one of the most popular studies today, using sentiment analysis companies can analyze customer satisfaction to improve their services. This study aims to analyze airline sentiments with five different features such as pragmatic, lexical n-gram, POS, sentiment, and LDA using the Support Vector Machine and Maximum Entropy methods. The best results can be obtained using the Maximum Entropy method using all feature extraction with an accuracy of 92.7% and in the Support Vector Machine method, the accuracy obtained is 89.2%.

scholarly journals SENTIMENT ANALYSIS ON TWITTER BY USING MAXIMUM ENTROPY AND SUPPORT VECTOR MACHINE METHOD

SINERGI ◽  
2020 ◽  
Vol 24 (2) ◽  
pp. 87
Author(s):  
Mona Cindo ◽  
Dian Palupi Rini ◽  
Ermatita Ermatita
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Product Quality ◽  
Sentiment Analysis ◽  
Maximum Entropy ◽  
The Other ◽  
Classification Method ◽  
Support Vector ◽  
General Opinion

With the advancement of social media and its growth, there is a lot of data that can be presented for research in social mining. Twitter is a microblogging that can be used. In this event, a lot of companies used the data on Twitter to analyze the satisfaction of their customer about product quality. On the other hand, a lot of users use social media to express their daily emotions. The case can be developed into a research study that can be used both to improve product quality, as well as to analyze the opinion on certain events. The research is often called sentiment analysis or opinion mining. While The previous research does a particularly useful feature for sentiment analysis, but it is still a lack of performance. Furthermore, they used Support Vector Machine as a classification method. On the other hand, most researchers found another classification method, which is considered more efficient such as Maximum Entropy. So, this research used two types of a dataset, the general opinion data, and the airline's opinion data. For feature extraction, we employ four feature extraction, such as pragmatic, lexical-grams, pos-grams, and sentiment lexical. For the classification, we use both of Support Vector Machine and Maximum Entropy to find the best result. In the end, the best result is performed by Maximum Entropy with 85,8% accuracy on general opinion data, and 92,6% accuracy on airlines opinion data.

scholarly journals Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM)

2020 ◽  
Vol 4 (3) ◽  
pp. 650
Author(s):  
Rian Tineges ◽  
Agung Triayudi ◽  
Ira Diana Sholihati
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Classification Model ◽  
Support Vector ◽  
Internet Service ◽  
Active User ◽  
The Social ◽  
Twitter Users ◽  
Use Of The Internet

In the year 2018, 18.9% of the population in Indonesia mentioned that the main reason for their use of the Internet is social media. One of the social media with an active user of 6.43 million users is Twitter. Based on the surge of information published via Twitter, it is possible that such information may contain the user's opinions on an object, such objects may be events around the community such as a product or service. This makes the company use Twitter as a medium to disseminate information. An example is an Internet Service Provider (ISP) such as Indihome. Through Twitter, users can discuss each other's complaints or satisfaction with Indihome's services. It takes a method of sentiment analysis to understand whether the textual data includes negative opinions or positive opinions. Thus, the authors use the Support Vector Machine (SVM) method in sentiment analysis on the opinions of the Indihome service user on Twitter, with the aim of obtaining a sentiment classification model using SVM, and to know how much accuracy the SVM method generates, which is applied to sentiment analysis, and to see how satisfied the Indihome service users are based on Twitter. After testing with SVM method The result is accuracy 87%, precision 86%, recall 95%, error rate 13%, and F1-score 90%

scholarly journals Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia Menggunakan Algoritma Support Vector Machine (SVM)

2021 ◽  
Vol 8 (1) ◽  
pp. 147
Author(s):  
Primandani Arsi ◽  
Retno Waluyo
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Public Discourse ◽  
Capital City ◽  
Support Vector ◽  
The Public ◽  
Machine Learning Methods ◽  
Classification Technique

<p class="Abstrak">Dewasa ini, media sosial berkembang pesat di internet, salah satu yang banyak digemari adalah Twitter. Berbagai topik ramai diperbincangkan di Twitter mulai dari ekonomi, politik, sosial, budaya, hukum dan lain-lain. Salah satu topik yang ramai diperbincangkan di Twitter adalah terkait isu pemindahan ibu kota Indonesia. Namun dibalik hal tersebut terdapat kontroversi dari  pihak yang merasa  pro dan kontra, masing-masing memiiki sudut pandang yang berbeda.  Hal ini menyebabkan munculnya fenomena perdebatan khususnya di Twitter yang sebenarnya menunjukkan perhatian kolektif mengenai wacana publik tersebut. Analisis sentimen adalah proses mengekstraksi, memahami dan mengolah data berupa teks yang tidak terstruktur secara otomatis guna mendapatkan informasi sentimen yang terdapat pada sebuah kalimat pendapat atau opini. Dalam penerapan analisis sentimen menggunakan metode <em>machine learning</em> terdapat beberapa metode yang sering digunakan. Dalam penelitian ini diusulkan metode <em>Support Vector Machine</em> (SVM) untuk diterapkan pada <em>tweets</em> topik pemindahan ibu kota Indonesia untuk tujuan klasifikasi kelas sentimen pada media sosial <em>twitter</em>. Teknis klasifikasi  dilakukan dengan cara mengklasifikasikan menjadi 2 kelas yakni positif dan negatif. Berdasarkan hasil pengujian yang dilakukan terhadap <em>tweets</em> sentimen pemindahan ibu kota dari media sosial twitter sebanyak 1.236 <em>tweets</em> (404 positif dan 832 negatif) menggunakan SVM diperoleh akurasi =96,68%, <em>precision=</em>95.82%, <em>recall</em>=94.04% dan AUC = 0,979.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstrak"><em><em>Today, social media is growing fast on the internet<span lang="EN-GB">.</span><span lang="EN-GB">On</span>e of the most popular<span lang="EN-GB"> social media</span> is Twitter. Many topics are discussed on Twitter such as economic, politic, socia<span lang="EN-GB">l</span>, cultur<span lang="EN-GB">e</span>, <span lang="EN-GB">and l</span>aw<span lang="EN-GB">.</span> One of the hot topics discussed on Twitter is the issue of relocating Indonesia's capital city. However<span lang="EN-GB">, </span>there is controversy from supporters and opponents<span lang="EN-GB">. They</span> have different views. <span lang="EN-GB">This issue leads to</span> a phenomenon of debate on Twitter <span lang="EN-GB">that </span>actually show<span lang="EN-GB">s a </span>collective concern about the public discourse. Sentiment analysis is a process of extracting, understand<span lang="EN-GB">ing </span>and process<span lang="EN-GB">ing</span> unstructured data to get sentiment information which is<span lang="EN-GB"> found</span> in an opinion sentence. Application of sentiment analysis using machine learning methods<span lang="EN-GB"> shows that</span> there are several methods that are often used. In this study, the Support Vector Machine (SVM) method is proposed to be applied to tweets on the topic of relocating Indonesia's capital city for sentiment classification on social media twitter. The classification technique is carried out into 2 classes, namely positive and negative. Based on testing on the sentiment of relocating Indonesia's capital city from social media twitter from 1,116 tweets (404 positive and 832 negative) using SVM obtained accuracy = 96.68%, precision = 95.82%, recall = 94.04% and AUC = 0.979.</em></em></p>

scholarly journals A Comparative Study of Different Classification Techniques for Sentiment Analysis

2020 ◽  
Vol 11 (1) ◽  
pp. 49-57
Author(s):  
Soumadip Ghosh ◽  
Arnab Hazra ◽  
Abhishek Raj
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Decision Tree ◽  
Comparative Study ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Support Vector ◽  
Product Reviews ◽  
Accurate Model

Sentiment analysis denotes the analysis of emotions and opinions from text. The authors also refer to sentiment analysis as opinion mining. It finds and justifies the sentiment of the person with respect to a given source of content. Social media contain vast amounts of the sentiment data in the form of product reviews, tweets, blogs, and updates on the statuses, posts, etc. Sentiment analysis of this largely generated data is very useful to express the opinion of the mass in terms of product reviews. This work is proposing a highly accurate model of sentiment analysis for reviews of products, movies, and restaurants from Amazon, IMDB, and Yelp, respectively. With the help of classifiers such as logistic regression, support vector machine, and decision tree, the authors can classify these reviews as positive or negative with higher accuracy values.

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